Multi-agent deep reinforcement learning concept for mobile cyber-physical systems control

Abstract

High complexity of mobile cyber physical systems (MCPS) dynamics makes it difficult to apply classical methods to optimize the MCPS agent management policy. In this regard, the use of intelligent control methods, in particular, with the help of artificial neural networks (ANN) and multi-agent deep reinforcement learning (MDRL), is gaining relevance. In practice, the application of MDRL in MCPS faces the following problems: 1) existing MDRL methods have low scalability; 2) the inference of the used ANNs has high computational complexity; 3) MCPS trained using existing methods have low functional safety. To solve these problems, we propose the concept of a new MDRL method based on the existing MADDPG method. Within the framework of the concept, it is proposed: 1) to increase the scalability of MDRL by using information not about all other MCPS agents, but only about n nearest neighbors; 2) reduce the computational complexity of ANN inference by using a sparse ANN structure; 3) to increase the functional safety of trained MCPS by using a training set with uneven distribution of states. The proposed concept is expected to help address the challenges of applying MDRL to MCPS. To confirm this, it is planned to conduct experimental studies.